Rare Eye Diseases Automatic Classification: A Deep Learning Approach

Two rare genetic eye disorders, known as Retinitis Pigmentosa (RP) and Stargardt Disease (STGD), both falling under the classification of Inherited Retinal Diseases (IRDs), have emerged as focal points of investigation in the pursuit of potential treatments leveraging cutting-edge technologies, notably artificial intelligence (AI) integrated with fundoscopy. These IRDs, characterized by their genetic underpinnings, serve as poignant reminders of the intricate and multifaceted nature of genetic eye disorders. The primary objective of this work was to develope an algorithm capable of automatically categorizing fundoscopies obtained from 74 pediatric eyes. In pursuit of this goal, an artificial intelligence algorithm was developed, exploiting the YOLOv8n Net. Through rigorous testing, it was demonstrated that this algorithm effectively and accurately classified the samples within the test set, exhibiting a notable absence of misclassification errors. The overarching ambition of this study is to introduce a robust and reliable classification tool that can significantly enhance the diagnostic process for rare diseases such as RP and STGD. By leveraging the power of advanced technologies, particularly AI, this research endeavors to streamline and optimize diagnostic procedures, thereby offering hope for improved management and treatment outcomes for individuals affected by these challenging conditions.